Inicio  /  Clean Technologies  /  Vol: 5 Par: 1 (2023)  /  Artículo
ARTÍCULO
TITULO

A Comprehensive Model to Estimate Electric Vehicle Battery?s State of Charge for a Pre-Scheduled Trip Based on Energy Consumption Estimation

Quynh T. Tran    
Leon Roose    
Chayaphol Vichitpunt    
Kumpanat Thongmai and Krittanat Noisopa    

Resumen

EV development is being prioritized in order to attain the target of net zero emissions by 2050. Electric vehicles have the potential to decrease greenhouse gas (GHG) emissions, which contribute to global warming. The driving range of electric vehicles is a significant limitation that prevents people from using them generally. This paper proposes a comprehensive model for calculating the amount of energy needed to charge EVs for a scheduled trip. The model contains anticipated consumption energy for the whole trip as well as contingency energy to account for unpredictable conditions. The model is simple to apply to various types of electric vehicles and produces results with sufficient precision. A number of driving tests with different road characteristics and weather conditions were implemented to evaluate the success of the proposed method. The findings could help the users feel more confidence when driving EVs, promote the usage of EVs, and advocate for the increased use of green and renewable energy sources.

 Artículos similares

       
 
Ai-Sheng Wang, Zhang-Cai Yin and Shen Ying    
The possibility of moving objects accessing different types of points of interest (POIs) at specific times is not always the same, so quantitative time geography research needs to consider the actual POI semantic information, including POI attributes and... ver más

 
Adedamola Adesokan, Rowan Kinney and Eirini Eleni Tsiropoulou    
This paper tackles the challenges inherent in crowdsourcing dynamics by introducing the CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select suitable crowdsourcers while contributing information to crowdsourcing tasks, CROWDMATCH ... ver más
Revista: Future Internet

 
Jing Liu, Xuesong Hai and Keqin Li    
Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their local data dir... ver más
Revista: Future Internet

 
Dominik Warch, Patrick Stellbauer and Pascal Neis    
In the digital transformation era, video media libraries? untapped potential is immense, restricted primarily by their non-machine-readable nature and basic search functionalities limited to standard metadata. This study presents a novel multimodal metho... ver más
Revista: Future Internet

 
Gulshan Saleem, Usama Ijaz Bajwa, Rana Hammad Raza and Fan Zhang    
Surveillance video analytics encounters unprecedented challenges in 5G and IoT environments, including complex intra-class variations, short-term and long-term temporal dynamics, and variable video quality. This study introduces Edge-Enhanced TempoFuseNe... ver más
Revista: Future Internet